Join Mirendil as a research engineer to build evaluation infrastructure for frontier AI models.
Posted by employer 4 months ago
First seen on Joblaze 2 months ago
Last verified on the company career page 1 day ago
Skills & Technologies
AI in the day-to-day
null
Not disclosed in this posting: years of experience, work arrangement, visa sponsorship.
Benefits
Joblaze summary
In this role, the research engineer focuses on developing evaluation frameworks and automated pipelines to assess the performance of AI models, ensuring they meet specific capabilities. Key skills include expertise in model evaluation, automation, and observability tools, as well as a strong understanding of AI research methodologies. This position is ideal for experienced professionals with a background in AI or machine learning who are passionate about advancing model evaluation techniques. Mirendil is dedicated to pioneering AI research, making this a unique opportunity to contribute to cutting-edge developments.
Joblaze insights
- Listed about 2 months ago — first seen on Joblaze September 3, 2026. Last confirmed on Mirendil's careers page November 10, 2026.
- Salary band is above the typical range for AI/ML roles (median ~$189,308).
- Starts above 63% of 68 comparable staff ai/ml roles that list AI/ML we track (median $248,000 across 24 companies). See AI/ML salary trends
- AI/ML appears in 34.1% of 457 comparable staff ai/ml roles.
Quick facts
- What's the salary range?
- Mirendil lists $300,000–$400,000 for this role.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: AI/ML.
- What seniority level is this role?
- Mirendil targets staff-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Member of Technical Staff, Model Evaluation role at Mirendil.
From the original posting
The Role
We are looking for a research engineer to build the evaluation infrastructure that tells us whether our models are getting better in ways we care about. You'll own the frameworks, pipelines, and tooling that measure model behavior across capabilities. Some example areas you might work on (not limited to):
Design and build evaluation frameworks that measure model capabilities along realistic axes, beyond standard benchmarks.
Build automated eval pipelines and regression-detection systems that run continuously and surface signal quickly.
Develop agent-assisted workflows for humans to efficiently inspect model behavior.
Instrument training runs with observability tooling so researchers can understand what's changing in model behavior, and why.
Partner with post-training and RL teams to close the loop between eval signal and training decisions.
If you're excited about the hard problem of knowing whether a frontier AI system is actually improving, we'd love to hear from you.
Standard company text repeated across Mirendil's postings is omitted here.
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